Football (and Hurling to an even bigger extent) operates in the universe of small numbers. Dublin & Kerry have played each other in just 25 Championship games in the GAA’s 125+ year history. In a very similar period the Yankees have played the Red Sox 2,130 times!! Comparing era’s and teams in that historical rivalry carries much more weight than the one on this island.

Every year football team’s seasons seem to be defined by one or two very small, possible random, events. In 2015 alone there have been some great examples of this;

Westmeath were 8 points down at half-time in a seemingly one-sided Leinster semi-final. Meath capitulated and Westmeath went on to win by 4. The first time the have ever beaten their near rivals. The following day’s papers were full of headlines like; Tom Cribbin’s tough love pays off for Westmeath, following is very public criticism of his players during the national league. This all despite the media being heavily critical of the ‘rant’ at the time, in hindsight it seemed they were happy to change their mind. By most accounts Westmeath’s season was heralded as a success despite one second half performance and going on to only score a further 13 points in their remaining 2 games.

Fermanagh another team being picked as a ‘weaker’ county punching above it’s weight this year, beat Antrim twice, granted had a very good win against an out-of-sorts Roscommon team and had a moral victory against Dublin.

At the other end of the scale Cork had a terrible year, resulting in the resignation of their manager. Despite pushing Kerry to the pin of their collar in Killarney, Kerry required a lucky inspirational point in the dying minutes to force a replay. Cork couldn’t seem to raise themselves for the replay or the qualifier game against Kildare 7 days later.

Dealing in these tiny sample sizes makes it very difficult to separate the signal from the noise. How much of that Westmeath 2nd half display was down to a managerial rant 3 months previous, or tactical changes at the break? Was Fionn Fitzgerald’s point lucky or calm execution of skill honed on the fields in the Kingdom.?

Expected Points (ExpP)

Shots is perhaps the easiest performance characteristic to attempt to separate the signal from the noise. To do this we can examine what the ‘average’ footballer would expect given the same shot location. This metric is similar to those found in most sports. Soccer has expected goal metrics and Basketball has an Expected field goal metric as just 2 examples.

I have broken the pitch into 5 x 5 square zones. Giving 13 zones long and 18 zones wide (234 zones in total). We then take the average return (Scores / Attempts) for each zone and work out what the expected return for the ‘average’ footballer from that zone would be.

expP-pitch-map

Because of the difference between shots from play and placed ball we need to create 2 set’s of ExpP. Actually we create another one for Penalty’s.

Let’s look at an example; Taking a free in front of the goal and on the 21m line (zone 5;8) gives a 100% expected return for the ‘average’ footballer. We would not expect anybody to miss from here. If, hypothetically, a team took 10 frees from this position we would expect them to score 10 points.

If we look at the same zone but from play we would expect the ‘average’ footballer to score 50% of the time. If our hypothetical team took 10 shots from this location we would expect them to score 5 points.

We can plot the shot location of every shot for each team – we add all the expected returns to see what a team was expected to score based on the chances they created.

In any one game a team will score more or less than the ExpP but this is randomness – the ExpP should give us a more accurate reflection of what the true value of the chances created was.

Fionn Fitzgerald

So to bring us back the the Fionn Fitzgerald shot we can look at the true value of that shot. When I plot that shot it goes into zone 9;13. The average return from here is 27%!!. To put this another way; that shot only just over 1 in 4 times.The reason we like sport is because sometimes we don’t know what will happen and there are no second chances under the same conditions. Cometh the hour cometh the man and all that. The shot sails over the bar, Kerry live to fight another day, their manager becomes a tactical genius again and the show moves on. However Cork can count them somewhat unlucky based on that single event. If you pause the game at that point Cork are the overwhelming favourites every-time.

Donegal v Mayo

Let’s take a look at the model on a team level. If we take the recent game between Mayo and Donegal we can see what each team was likely to score based on the chances they created. Dealing with goals presents a slightly different problem than other sports with a success/failure scoring system. If a shot is taken inside the red zone (above) I am making (a possibly unrealistic) assumption that this was a goal scoring chance. For chances in these 12 zones I multiply the expected return x 3 to more accurately account for goal chances. This is not perfect but it’s a start.

Donegal: The model shows Donegal’s ExpP was 13.3 (they scored 11). That’s pretty close. The reason for the over estimation is probably that Donegal had 2 goal-scoring chances with an ExpP of 3.2 but took neither.

Mayo: The model shows Mayo’s ExpP was 17.3 (they scored 19). Again not very far off. The Lee Keegan goal brings the actual above the ExpP. My model assumes this is a shot for a point (as it falls outside the 12 goal scoring zones) so therefore underestimates the ExpP.

Just to check I looked at the Monaghan v Tyrone game.

Monaghan scored 14 (ExpP = 11.7). Shows just how off they were on the day. Even with a bit of luck they fell short.

Tyrone scored 15 (ExpP = 15.9). As good as Tyrone were on another day they could have scored slightly more.

Final Thoughts

This is only a starting point, in no way am I saying this is perfect. But it’s a hell of a lot better than spouting on again about desire and workrate.

 

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.